Adaptive pose and location estimation for indoor mobile robot

Author

Chen, Cheng ; Wang, Han

Author_Institution

Nanyang Technol. Univ., Singapore

Volume

2

fYear

2003

fDate

12-15 Oct. 2003

Firstpage

1616

Abstract

In this paper, a new method for mobile robot positioning is proposed. The method is a combination of particle filter (PF) and extended Kalman filter (EKF). Under normal driving situation, EKF is sufficient to estimate the vehicle´s pose and location. Subject to external disturbances, EKF does not converge from time to time. PF is then introduced and the switching criteria are governed by the estimation confidence. We cluster the particles of PF into groups at the end of each iteration. The number of clusters is used as one of the parameters to determine whether the PF has converged. In the paper, the formulations and algorithms are illuminated and experimental results are also given and analyzed.

Keywords

Gaussian distribution; Hough transforms; Kalman filters; mobile robots; position control; extended Kalman filter; indoor mobile robot; mobile robot positioning; particle filter; switching criteria; vehicle location estimation; vehicle pose estimation; Clustering algorithms; Feature extraction; Hospitals; Mobile robots; Particle filters; Robot sensing systems; Sensor phenomena and characterization; Sonar detection; Switches; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Transportation Systems, 2003. Proceedings. 2003 IEEE

Print_ISBN

0-7803-8125-4

Type

conf

DOI

10.1109/ITSC.2003.1252757

Filename

1252757